The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a novel algorithm, named adaptive morphological lifting wavelet (AMLW). It is the first time to be applied for the detection of power quality disturbances (PQD). AMLW is a nonlinear wavelet transform which is based on morphological operation and the lifting scheme. The adaptability of AMLW lies in that the algorithm can select between two filters, the morphological gradient filter...
When a power system is contaminated by power quality problems, a short transition will appear before power system restoring to a new stability state. The short transitions of various power disturbances correspond to different principal component characteristics. According to these characteristics, in this paper, a scheme based on transient behaviors using morphological max-lifting scheme (MMLS) and...
This paper proposes a novel method for power quality (PQ) disturbance detection, based on morphology singular entropy (MSE). MSE consists of three techniques, i.e., mathematical morphology (MM), singular value decomposition (SVD) and entropy theory. The proposed method firstly utilizes MM to obtain the filtered outputs of the original signal at different levels. Then a matrix composed by the outputs...
This paper proposes a method for identification of power quality (PQ) disturbances using morphological pattern spectrum (MPS) and probabilistic neural network (PNN). The PQ disturbance signals are decomposed by a three-order MPS to extract a number of features which are used for disturbance identification. These features compose a feature vector to train PNN classifier. The trained PNN is employed...
This paper has proposed a novel power quality (PQ) analysis method using morphological entropy. The method comprises two stages. First, a voltage or current signal is decomposed by the morphological wavelet transform of hit-or-miss wavelet, which involves the improved hit-or-miss transform in the decomposition process. Afterwards, the hit-or-miss wavelet singular entropy (HMWSE), which is developed...
This paper presents a morphological max-lifting scheme for the detection and classification of low-frequency power disturbances. In order to extract waveform features of low-frequency disturbances, the proposed scheme employs mathematical morphology (MM) for its advantage in noise removing and max-lifting for its ability of information preserving. Afterwards, two aided variables are constructed to...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.